Abstract
In this digital age, personal data is more valuable than an actual human being. It corresponds to a person’s daily internet activity. Open-source intelligence can be used to collect data in a variety of ways, which is later put to use for social engineering attacks. The purpose of this paper is to assess the level of data that is freely available online and its potential consequences for personal privacy. This paper discusses the use of machine learning algorithms and tools for automating email phishing attacks. The personal data of the victim is gathered from open-source websites in order to analyze their online activity. This information is then analyzed in an attempt to learn more about the victim’s interests and a relevant email template is created based on this information. The machine learning algorithm is then provided with the constructed template, which predicts how successful a phishing attack would be if launched. This paper uses a machine learning algorithm that is composed of support vector machines (SVM) and logistic regression (LR). This hybrid algorithm, which is a combination of SVM and logistic regression, achieves a peak accuracy of 99.69% when compared to using only one type of classification method, such as SVM or LR. The purpose of this paper is to increase the effectiveness of phishing attacks by automating the data extraction process and to analyse the success rate of attack using machine learning before launching it. This paper will serve the interests of institutions/companies by providing a convenient way to conduct automatic phishing as part of Cyber-Security training to educate employees, giving them a practical experience of social engineering attack.
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Hegde, A.M., Kumar, S.B., Bhuvantej, R., Vyshak, R., Sarasvathi, V. (2023). Spear Phishing Using Machine Learning. In: Singh, M., Tyagi, V., Gupta, P., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2023. Communications in Computer and Information Science, vol 1848. Springer, Cham. https://doi.org/10.1007/978-3-031-37940-6_43
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